The binary descriptors are the representation of choice for real-time keypoint matching. However, they suffer from reduced matching rates due to their discrete nature. We propose an approach that can augment their performance by searching in the top K near neighbor matches instead of just the single nearest neighbor one. To pick the correct match out of the K near neighbors, we exploit statisti...

This paper presents a new algorithm to answer k -nearest neighbor queries called the Fractal k -Nearest Neighbor (k NNF ()). This algorithm takes advantage of the fractal dimension of the dataset under scan to estimate a suitable radius to shrinks a query that retrieves the k -nearest neighbors of a query object. k -NN() algorithms starts searching for elements at any distance from the query ce...

The M-tree and its variants have been proved to provide an efficient similarity search in database environments. In order to further improve their performance, in this paper we propose an extension of the M-tree family, which makes use of nearest-neighbor (NN) graphs. Each tree node maintains its own NN-graph, a structure that stores for each node entry a reference (and distance) to its nearest...

Spatial query which focus only on the geometrics properties of an object like points, rectangle etc. Now a day‟s many new applications which involve the queries that completely aim to return an object which satisfy equally on spatial predicate and their associated text. Spatial query takes the given location and a keyword as the input and finds the object that matches the both spatial predicate...

Lazy classifiers store all of the training samples and do not build a classifier until a new sample needs to be classified. It differs from eager classifiers, such as decision tree induction, which build a general model (such as a decision tree) before receiving new samples. K-nearest neighbor (KNN) classification is a typical lazy classifier. Given a set of training data, a knearest neighbor c...